D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 33 Citations 6,196 65 World Ranking 6852 National Ranking 663

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

Lequan Yu mainly focuses on Artificial intelligence, Segmentation, Computer vision, Convolutional neural network and Pattern recognition. His work deals with themes such as Machine learning and Residual, which intersect with Artificial intelligence. The Machine learning study combines topics in areas such as Gold standard and Medical imaging.

His research on Segmentation focuses in particular on Image segmentation. His work on Medical image computing as part of general Computer vision research is frequently linked to Colonoscopy, thereby connecting diverse disciplines of science. His study of Discriminative model is a part of Pattern recognition.

His most cited work include:

  • Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks (409 citations)
  • Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks (356 citations)
  • VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images (349 citations)

What are the main themes of his work throughout his whole career to date?

Lequan Yu spends much of his time researching Artificial intelligence, Segmentation, Pattern recognition, Machine learning and Deep learning. His study in the field of Image segmentation, Convolutional neural network and Artificial neural network also crosses realms of Domain. When carried out as part of a general Segmentation research project, his work on Scale-space segmentation is frequently linked to work in Process, therefore connecting diverse disciplines of study.

In general Pattern recognition, his work in Discriminative model and Feature extraction is often linked to Fundus linking many areas of study. His Machine learning research integrates issues from Contextual image classification and Image. Lequan Yu works mostly in the field of Deep learning, limiting it down to concerns involving Training set and, occasionally, Test set.

He most often published in these fields:

  • Artificial intelligence (92.78%)
  • Segmentation (48.45%)
  • Pattern recognition (43.30%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (92.78%)
  • Machine learning (35.05%)
  • Segmentation (48.45%)

In recent papers he was focusing on the following fields of study:

Artificial intelligence, Machine learning, Segmentation, Image segmentation and Deep learning are his primary areas of study. Artificial intelligence is closely attributed to Pattern recognition in his research. His work carried out in the field of Machine learning brings together such families of science as Contextual image classification, Brain magnetic resonance imaging and Benchmark.

His Segmentation research is multidisciplinary, incorporating elements of Entropy, Entropy and Convolutional neural network. As a member of one scientific family, Lequan Yu mostly works in the field of Image segmentation, focusing on Annotation and, on occasion, Margin. His Deep learning study combines topics in areas such as Semi-supervised learning, Co-training, Robustness and Medical imaging.

Between 2019 and 2021, his most popular works were:

  • MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data (30 citations)
  • Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation (30 citations)
  • CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading (30 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His primary areas of investigation include Artificial intelligence, Machine learning, Image segmentation, Deep learning and Segmentation. The Artificial neural network, Interpolation and Metal Artifact research he does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Domain and Image restoration, therefore creating a link between diverse domains of science. His Machine learning study integrates concerns from other disciplines, such as Contextual image classification and Image.

His research in Image segmentation focuses on subjects like Robustness, which are connected to Normalization, Magnetic resonance imaging, MS-Net and Supervised learning. His research investigates the connection between Deep learning and topics such as Medical imaging that intersect with issues in Pattern recognition and Regularization. His studies in Segmentation integrate themes in fields like Semi-supervised learning and Co-training.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

Lequan Yu;Hao Chen;Qi Dou;Jing Qin.
IEEE Transactions on Medical Imaging (2017)

500 Citations

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

Qi Dou;Hao Chen;Lequan Yu;Lei Zhao.
IEEE Transactions on Medical Imaging (2016)

480 Citations

VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images

Hao Chen;Qi Dou;Lequan Yu;Jing Qin.
NeuroImage (2017)

430 Citations

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

Hao Chen;Xiaojuan Qi;Lequan Yu;Pheng-Ann Heng.
computer vision and pattern recognition (2016)

373 Citations

Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection

Qi Dou;Hao Chen;Lequan Yu;Jing Qin.
IEEE Transactions on Biomedical Engineering (2017)

363 Citations

3D deeply supervised network for automated segmentation of volumetric medical images.

Qi Dou;Lequan Yu;Hao Chen;Yueming Jin.
Medical Image Analysis (2017)

328 Citations

DCAN: Deep contour-aware networks for object instance segmentation from histology images

Hao Chen;Xiaojuan Qi;Lequan Yu;Qi Dou.
Medical Image Analysis (2017)

268 Citations

Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images.

Lequan Yu;Xin Yang;Hao Chen;Jing Qin.
national conference on artificial intelligence (2017)

236 Citations

Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

Jorge Bernal;Nima Tajkbaksh;Francisco Javier Sanchez;Bogdan J. Matuszewski.
IEEE Transactions on Medical Imaging (2017)

186 Citations

3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

Qi Dou;Hao Chen;Yueming Jin;Lequan Yu.
medical image computing and computer assisted intervention (2016)

174 Citations

Best Scientists Citing Lequan Yu

Pheng-Ann Heng

Pheng-Ann Heng

Chinese University of Hong Kong

Publications: 118

Qi Dou

Qi Dou

Chinese University of Hong Kong

Publications: 71

Dinggang Shen

Dinggang Shen

ShanghaiTech University

Publications: 60

Dong Ni

Dong Ni

Shenzhen University

Publications: 42

Nasir M. Rajpoot

Nasir M. Rajpoot

University of Warwick

Publications: 41

Yefeng Zheng

Yefeng Zheng

Tencent (China)

Publications: 38

Jing Qin

Jing Qin

Hong Kong Polytechnic University

Publications: 36

Xin Yang

Xin Yang

Sun Yat-sen University

Publications: 34

Alan L. Yuille

Alan L. Yuille

Johns Hopkins University

Publications: 34

Hao Chen

Hao Chen

Chinese University of Hong Kong

Publications: 33

Tianfu Wang

Tianfu Wang

Shenzhen University

Publications: 28

Nassir Navab

Nassir Navab

Technical University of Munich

Publications: 28

Danny Z. Chen

Danny Z. Chen

University of Notre Dame

Publications: 27

Yong Xia

Yong Xia

Northwestern Polytechnical University

Publications: 26

Huazhu Fu

Huazhu Fu

Agency for Science, Technology and Research

Publications: 22

Daniel Rueckert

Daniel Rueckert

Technical University of Munich

Publications: 22

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

If you think any of the details on this page are incorrect, let us know.

Contact us
Something went wrong. Please try again later.